Advanced procedures and scheduling for aircraft assembly processes: A systematic review approach
Bibliographic record
Abstract
The target of the study is to conduct a systematic review of the scientific literature published over the last 20 years on advanced procedures and scheduling of aircraft assembly processes. The bibliography analysed includes the 25 most cited references during this period, published in English, open access and fee-paying, applicable to the aeronautical field, and published in aeronautical, mechanical, or manufacturing engineering journals, and with a Q1 (80%) and Q2 (20%) classification by JCR impact factor. The final selection of articles has a total number of 806 citations and a total average of 32.24 citations/article. Forty percent of the selected articles are focused on the optimisation of the assembly process planning, 28% are aimed at improving the assembly process by developing the machining process of the individual components that constitute the assembly, and 20% of the studies address their research to the automation of the process by implementing robots. In addition, 16% of the selected articles include the proposal of new algorithms and process models, and 12% involve the development of new ad hoc software, with the integration of the developed software into existing software commonly used in the aeronautical sector, such as CATIA by Dassault Systèmes. On the other hand, 52% of the works include within the study the analysis of a case on an industrial scale in the aeronautical sector such as engines, fuselages, aircraft wings and tails, satellites, and final assembly lines. Finally, regarding the origin of the studies, China is the country with the highest percentage of publications on this topic, accounting for 40% of the selected articles, followed by the United Kingdom in second place with 16% and Canada in third place with 12%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.040 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".